Idiom-Aware Compositional Distributed Semantics

Idioms are peculiar linguistic constructions that impose great challenges for representing the semantics of language, especially in current prevailing end-to-end neural models, which assume that the semantics of a phrase or sentence can be literally composed from its constitutive words. In this paper, we propose an idiomaware distributed semantic model to build representation of sentences on the basis of understanding their contained idioms. Our models are grounded in the literalfirst psycholinguistic hypothesis, which can adaptively learn semantic compositionality of a phrase literally or idiomatically. To better evaluate our models, we also construct an idiom-enriched sentiment classification dataset with considerable scale and abundant peculiarities of idioms. The qualitative and quantitative experimental analyses demonstrate the efficacy of our models. The newly-introduced datasets are publicly available at http: //nlp.fudan.edu.cn/data/